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Four different artificial neural network architectures have been tested for their suitability to extract and predict sequence features. For optimization of the network weights an evolutionary computing method has been applied. The networks have feedforward architecture and provide adaptive neural filter systems for pattern recognition in primary structures(More)
The theory of artificial neural networks is briefly reviewed focusing on supervised and unsupervised techniques which have great impact on current chemical applications. An introduction to molecular descriptors and representation schemes is given. In addition, worked examples of recent advances in this field are highlighted and pioneering publications are(More)
Cleavage sites in nuclear-encoded mitochondrial protein targeting peptides (mTPs) from mammals, yeast, and plants have been analysed for characteristic physicochemical features using statistical methods, perceptrons, multilayer neural networks, and self-organizing feature maps. Three different sequence motifs were found, revealing loosely defined arginine(More)
Artificial neural networks were used for extraction of characteristic physiochemical features from mitochondrial matrix metalloprotease target sequences. The amino acid properties hydrophobicity and volume were used for sequence encoding. A window of 12 residues was employed, encompassing positions -7 to +5 of precursors with cleavage sites. Two sets of(More)
A method for the rational design of locally encoded amino acid sequence features using artificial neural networks and a technique for simulating molecular evolution has been developed. De novo in machine design of Escherichia coli leader peptidase (SP1) cleavage sites serves as an example application. A modular neural network system that employs sequence(More)
De novo designed signal peptidase I cleavage sites were tested for their biological activity in vivo in an Escherichia coli expression and secretion system. The artificial cleavage site sequences were generated by two different computer-based design techniques, a simple statistical method, and a neural network approach. In previous experiments, a neural(More)
The potential of artificial neural filter systems for feature extraction from amino acid sequences is discussed. Analysis of signal peptidase I cleavage-sites in protein precursor sequences serves as an example application. Trained neural networks can be used as the fitness function in an evolutionary protein design cycle termed 'simulated molecular(More)
Initiator tRNAs have an anticodon loop conformation distinct from that of elongation tRNAs as detected by susceptibility to S1 nuclease. We now find the anticodon loop conformation of E. coli tRNAfMet to be stable under different salt conditions as detected by using S1 nuclease as a structural probe. In contrast, a conformational change is observed in the(More)